TY - GEN
T1 - The Bayesian Group-Lasso for analyzing contingency tables
AU - Raman, Sudhir
AU - Fuchs, Thomas J.
AU - Wild, Peter J.
AU - Dahl, Edgar
AU - Roth, Volker
PY - 2009
Y1 - 2009
N2 - Group-Lasso estimators, useful in many applications, suffer from lack of meaningful variance estimates for regression coefficients. To overcome such problems, we propose a full Bayesian treatment of the Group-Lasso, extending the standard Bayesian Lasso, using hierarchical expansion. The method is then applied to Poisson models for contingency tables using a highly efficient MCMC algorithm. The simulated experiments validate the performance of this method on artificial datasets with known ground-truth. When applied to a breast cancer dataset, the method demonstrates the capability of identifying the differences in interactions patterns of marker proteins between different patient groups.
AB - Group-Lasso estimators, useful in many applications, suffer from lack of meaningful variance estimates for regression coefficients. To overcome such problems, we propose a full Bayesian treatment of the Group-Lasso, extending the standard Bayesian Lasso, using hierarchical expansion. The method is then applied to Poisson models for contingency tables using a highly efficient MCMC algorithm. The simulated experiments validate the performance of this method on artificial datasets with known ground-truth. When applied to a breast cancer dataset, the method demonstrates the capability of identifying the differences in interactions patterns of marker proteins between different patient groups.
UR - http://www.scopus.com/inward/record.url?scp=71149093268&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:71149093268
SN - 9781605585161
T3 - Proceedings of the 26th International Conference On Machine Learning, ICML 2009
SP - 881
EP - 888
BT - Proceedings of the 26th International Conference On Machine Learning, ICML 2009
T2 - 26th International Conference On Machine Learning, ICML 2009
Y2 - 14 June 2009 through 18 June 2009
ER -